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importance. Acoustics-based diagnosis, using an array of microphones, is a promising method to identify faults, as sensing on the moving components in these high tech systems is not possible. The challenge is
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
neural network that conserves heat, mass, and salt across model components; 2) implementation of the network in the SIS2 sea ice model; 3) evaluation of the impact of the conservative network in fully
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Master's programmes with a strong research component. Our scientists conduct ground-breaking fundamental and applied research in the fields of Life and Health Science & Technology, Nanoscience, Chemical
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from being part of a larger team investigating Treg biology and Treg-focused immunotherapies in other diseases. The ideal candidate will have a strong background in cellular immunology and be proficient
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
Research Associate Vacancy ID PDS004277 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range Proposed Start Date 09
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bases, interventions and policies focused on sustainability and net zero transformations. By connecting sustainability with justice, research with action, and constituent knowledge producers and community
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-source computational tools in a project that aims to create life-saving technology to prevent devastating skull fractures in elderly populations. This unique position combines advanced finite element
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: ISTerre, LOCIE, and EDYTEM. The position is part of the ongoing development of an experimental platform dedicated to the mechanics of frozen rocks and soils. The postdoctoral fellow will work under the
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for this position is set according to the CNIO annual salary tables. The contract does not imply any obligation on the part of the CNIO after the 3-year contract has been terminated. Eligibility criteria Candidates
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Thun explores the possibility of high throughput materials development. In the context of our Materials Vision Tech initiative, we focus on multi-element gradient thin film systems, i.e. their rapid